Overview

Dataset statistics

Number of variables14
Number of observations374
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory41.0 KiB
Average record size in memory112.3 B

Variable types

Numeric9
Categorical5

Alerts

Unnamed: 0 is highly overall correlated with person_id and 5 other fieldsHigh correlation
person_id is highly overall correlated with Unnamed: 0 and 5 other fieldsHigh correlation
age is highly overall correlated with Unnamed: 0 and 5 other fieldsHigh correlation
sleep_duration is highly overall correlated with quality_of_sleep and 5 other fieldsHigh correlation
quality_of_sleep is highly overall correlated with sleep_duration and 4 other fieldsHigh correlation
physical_activity_level is highly overall correlated with daily_steps and 3 other fieldsHigh correlation
stress_level is highly overall correlated with sleep_duration and 6 other fieldsHigh correlation
heart_rate is highly overall correlated with sleep_duration and 7 other fieldsHigh correlation
daily_steps is highly overall correlated with physical_activity_level and 5 other fieldsHigh correlation
gender is highly overall correlated with Unnamed: 0 and 8 other fieldsHigh correlation
occupation is highly overall correlated with Unnamed: 0 and 10 other fieldsHigh correlation
bmi_category is highly overall correlated with physical_activity_level and 5 other fieldsHigh correlation
blood_pressure is highly overall correlated with Unnamed: 0 and 12 other fieldsHigh correlation
sleep_disorder is highly overall correlated with Unnamed: 0 and 10 other fieldsHigh correlation
Unnamed: 0 is uniformly distributedUniform
person_id is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
person_id has unique valuesUnique

Reproduction

Analysis started2023-09-18 14:55:34.331250
Analysis finished2023-09-18 14:56:03.728805
Duration29.4 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct374
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean186.5
Minimum0
Maximum373
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-09-18T14:56:03.917062image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.65
Q193.25
median186.5
Q3279.75
95-th percentile354.35
Maximum373
Range373
Interquartile range (IQR)186.5

Descriptive statistics

Standard deviation108.10874
Coefficient of variation (CV)0.57967154
Kurtosis-1.2
Mean186.5
Median Absolute Deviation (MAD)93.5
Skewness0
Sum69751
Variance11687.5
MonotonicityStrictly increasing
2023-09-18T14:56:04.594410image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
0.3%
246 1
 
0.3%
255 1
 
0.3%
254 1
 
0.3%
253 1
 
0.3%
252 1
 
0.3%
251 1
 
0.3%
250 1
 
0.3%
249 1
 
0.3%
248 1
 
0.3%
Other values (364) 364
97.3%
ValueCountFrequency (%)
0 1
0.3%
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
ValueCountFrequency (%)
373 1
0.3%
372 1
0.3%
371 1
0.3%
370 1
0.3%
369 1
0.3%
368 1
0.3%
367 1
0.3%
366 1
0.3%
365 1
0.3%
364 1
0.3%

person_id
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct374
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.5
Minimum1
Maximum374
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-09-18T14:56:04.913243image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.65
Q194.25
median187.5
Q3280.75
95-th percentile355.35
Maximum374
Range373
Interquartile range (IQR)186.5

Descriptive statistics

Standard deviation108.10874
Coefficient of variation (CV)0.57657995
Kurtosis-1.2
Mean187.5
Median Absolute Deviation (MAD)93.5
Skewness0
Sum70125
Variance11687.5
MonotonicityStrictly increasing
2023-09-18T14:56:05.255602image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
247 1
 
0.3%
256 1
 
0.3%
255 1
 
0.3%
254 1
 
0.3%
253 1
 
0.3%
252 1
 
0.3%
251 1
 
0.3%
250 1
 
0.3%
249 1
 
0.3%
Other values (364) 364
97.3%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
374 1
0.3%
373 1
0.3%
372 1
0.3%
371 1
0.3%
370 1
0.3%
369 1
0.3%
368 1
0.3%
367 1
0.3%
366 1
0.3%
365 1
0.3%

gender
Categorical

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Male
189 
Female
185 

Length

Max length6
Median length4
Mean length4.9893048
Min length4

Characters and Unicode

Total characters1866
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowMale
3rd rowMale
4th rowMale
5th rowMale

Common Values

ValueCountFrequency (%)
Male 189
50.5%
Female 185
49.5%

Length

2023-09-18T14:56:05.592758image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T14:56:05.900887image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 189
50.5%
female 185
49.5%

Most occurring characters

ValueCountFrequency (%)
e 559
30.0%
a 374
20.0%
l 374
20.0%
M 189
 
10.1%
F 185
 
9.9%
m 185
 
9.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1492
80.0%
Uppercase Letter 374
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 559
37.5%
a 374
25.1%
l 374
25.1%
m 185
 
12.4%
Uppercase Letter
ValueCountFrequency (%)
M 189
50.5%
F 185
49.5%

Most occurring scripts

ValueCountFrequency (%)
Latin 1866
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 559
30.0%
a 374
20.0%
l 374
20.0%
M 189
 
10.1%
F 185
 
9.9%
m 185
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1866
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 559
30.0%
a 374
20.0%
l 374
20.0%
M 189
 
10.1%
F 185
 
9.9%
m 185
 
9.9%

age
Real number (ℝ)

Distinct31
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.184492
Minimum27
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-09-18T14:56:06.166090image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum27
5-th percentile29.65
Q135.25
median43
Q350
95-th percentile58
Maximum59
Range32
Interquartile range (IQR)14.75

Descriptive statistics

Standard deviation8.6731335
Coefficient of variation (CV)0.20560005
Kurtosis-0.90977955
Mean42.184492
Median Absolute Deviation (MAD)7
Skewness0.25722214
Sum15777
Variance75.223244
MonotonicityIncreasing
2023-09-18T14:56:06.448950image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
43 34
 
9.1%
44 30
 
8.0%
37 20
 
5.3%
38 20
 
5.3%
50 20
 
5.3%
31 18
 
4.8%
32 17
 
4.5%
53 17
 
4.5%
59 16
 
4.3%
39 15
 
4.0%
Other values (21) 167
44.7%
ValueCountFrequency (%)
27 1
 
0.3%
28 5
 
1.3%
29 13
3.5%
30 13
3.5%
31 18
4.8%
32 17
4.5%
33 13
3.5%
34 2
 
0.5%
35 12
3.2%
36 12
3.2%
ValueCountFrequency (%)
59 16
4.3%
58 6
 
1.6%
57 9
2.4%
56 2
 
0.5%
55 2
 
0.5%
54 7
 
1.9%
53 17
4.5%
52 9
2.4%
51 8
 
2.1%
50 20
5.3%

occupation
Categorical

Distinct11
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Nurse
73 
Doctor
71 
Engineer
63 
Lawyer
47 
Teacher
40 
Other values (6)
80 

Length

Max length20
Median length17
Mean length7.2994652
Min length5

Characters and Unicode

Total characters2730
Distinct characters28
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st rowSoftware Engineer
2nd rowDoctor
3rd rowDoctor
4th rowSales Representative
5th rowSales Representative

Common Values

ValueCountFrequency (%)
Nurse 73
19.5%
Doctor 71
19.0%
Engineer 63
16.8%
Lawyer 47
12.6%
Teacher 40
10.7%
Accountant 37
9.9%
Salesperson 32
8.6%
Software Engineer 4
 
1.1%
Scientist 4
 
1.1%
Sales Representative 2
 
0.5%

Length

2023-09-18T14:56:06.765849image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
nurse 73
19.2%
doctor 71
18.7%
engineer 67
17.6%
lawyer 47
12.4%
teacher 40
10.5%
accountant 37
9.7%
salesperson 32
8.4%
software 4
 
1.1%
scientist 4
 
1.1%
sales 2
 
0.5%
Other values (2) 3
 
0.8%

Most occurring characters

ValueCountFrequency (%)
e 417
15.3%
r 337
12.3%
n 247
 
9.0%
o 215
 
7.9%
c 189
 
6.9%
a 166
 
6.1%
t 161
 
5.9%
s 145
 
5.3%
u 110
 
4.0%
i 77
 
2.8%
Other values (18) 666
24.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2344
85.9%
Uppercase Letter 380
 
13.9%
Space Separator 6
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 417
17.8%
r 337
14.4%
n 247
10.5%
o 215
9.2%
c 189
8.1%
a 166
 
7.1%
t 161
 
6.9%
s 145
 
6.2%
u 110
 
4.7%
i 77
 
3.3%
Other values (8) 280
11.9%
Uppercase Letter
ValueCountFrequency (%)
N 73
19.2%
D 71
18.7%
E 67
17.6%
L 47
12.4%
S 42
11.1%
T 40
10.5%
A 37
9.7%
R 2
 
0.5%
M 1
 
0.3%
Space Separator
ValueCountFrequency (%)
6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2724
99.8%
Common 6
 
0.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 417
15.3%
r 337
12.4%
n 247
 
9.1%
o 215
 
7.9%
c 189
 
6.9%
a 166
 
6.1%
t 161
 
5.9%
s 145
 
5.3%
u 110
 
4.0%
i 77
 
2.8%
Other values (17) 660
24.2%
Common
ValueCountFrequency (%)
6
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2730
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 417
15.3%
r 337
12.3%
n 247
 
9.0%
o 215
 
7.9%
c 189
 
6.9%
a 166
 
6.1%
t 161
 
5.9%
s 145
 
5.3%
u 110
 
4.0%
i 77
 
2.8%
Other values (18) 666
24.4%

sleep_duration
Real number (ℝ)

Distinct27
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1320856
Minimum5.8
Maximum8.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-09-18T14:56:07.084829image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum5.8
5-th percentile6
Q16.4
median7.2
Q37.8
95-th percentile8.4
Maximum8.5
Range2.7
Interquartile range (IQR)1.4

Descriptive statistics

Standard deviation0.79565673
Coefficient of variation (CV)0.11156018
Kurtosis-1.2865062
Mean7.1320856
Median Absolute Deviation (MAD)0.7
Skewness0.03755439
Sum2667.4
Variance0.63306963
MonotonicityNot monotonic
2023-09-18T14:56:07.406659image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
7.2 36
 
9.6%
6 31
 
8.3%
7.8 28
 
7.5%
6.5 26
 
7.0%
6.1 25
 
6.7%
7.7 24
 
6.4%
6.6 20
 
5.3%
7.1 19
 
5.1%
8.1 15
 
4.0%
7.3 14
 
3.7%
Other values (17) 136
36.4%
ValueCountFrequency (%)
5.8 2
 
0.5%
5.9 4
 
1.1%
6 31
8.3%
6.1 25
6.7%
6.2 12
 
3.2%
6.3 13
3.5%
6.4 9
 
2.4%
6.5 26
7.0%
6.6 20
5.3%
6.7 5
 
1.3%
ValueCountFrequency (%)
8.5 13
3.5%
8.4 14
3.7%
8.3 5
 
1.3%
8.2 11
 
2.9%
8.1 15
4.0%
8 13
3.5%
7.9 7
 
1.9%
7.8 28
7.5%
7.7 24
6.4%
7.6 10
 
2.7%

quality_of_sleep
Real number (ℝ)

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3128342
Minimum4
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-09-18T14:56:07.652773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile6
Q16
median7
Q38
95-th percentile9
Maximum9
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1969559
Coefficient of variation (CV)0.1636788
Kurtosis-0.74827554
Mean7.3128342
Median Absolute Deviation (MAD)1
Skewness-0.20744763
Sum2735
Variance1.4327035
MonotonicityNot monotonic
2023-09-18T14:56:07.890477image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
8 109
29.1%
6 105
28.1%
7 77
20.6%
9 71
19.0%
5 7
 
1.9%
4 5
 
1.3%
ValueCountFrequency (%)
4 5
 
1.3%
5 7
 
1.9%
6 105
28.1%
7 77
20.6%
8 109
29.1%
9 71
19.0%
ValueCountFrequency (%)
9 71
19.0%
8 109
29.1%
7 77
20.6%
6 105
28.1%
5 7
 
1.9%
4 5
 
1.3%

physical_activity_level
Real number (ℝ)

Distinct16
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.171123
Minimum30
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-09-18T14:56:08.146274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile30
Q145
median60
Q375
95-th percentile90
Maximum90
Range60
Interquartile range (IQR)30

Descriptive statistics

Standard deviation20.830804
Coefficient of variation (CV)0.35204341
Kurtosis-1.2660678
Mean59.171123
Median Absolute Deviation (MAD)15
Skewness0.074486903
Sum22130
Variance433.92238
MonotonicityNot monotonic
2023-09-18T14:56:08.381265image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
60 70
18.7%
30 68
18.2%
45 68
18.2%
75 67
17.9%
90 67
17.9%
40 6
 
1.6%
55 6
 
1.6%
35 4
 
1.1%
50 4
 
1.1%
70 3
 
0.8%
Other values (6) 11
 
2.9%
ValueCountFrequency (%)
30 68
18.2%
32 2
 
0.5%
35 4
 
1.1%
40 6
 
1.6%
42 2
 
0.5%
45 68
18.2%
47 1
 
0.3%
50 4
 
1.1%
55 6
 
1.6%
60 70
18.7%
ValueCountFrequency (%)
90 67
17.9%
85 2
 
0.5%
80 2
 
0.5%
75 67
17.9%
70 3
 
0.8%
65 2
 
0.5%
60 70
18.7%
55 6
 
1.6%
50 4
 
1.1%
47 1
 
0.3%

stress_level
Real number (ℝ)

Distinct6
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3850267
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-09-18T14:56:08.615807image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q14
median5
Q37
95-th percentile8
Maximum8
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7745264
Coefficient of variation (CV)0.32952974
Kurtosis-1.3273066
Mean5.3850267
Median Absolute Deviation (MAD)2
Skewness0.15432958
Sum2014
Variance3.1489441
MonotonicityNot monotonic
2023-09-18T14:56:08.854713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
3 71
19.0%
8 70
18.7%
4 70
18.7%
5 67
17.9%
7 50
13.4%
6 46
12.3%
ValueCountFrequency (%)
3 71
19.0%
4 70
18.7%
5 67
17.9%
6 46
12.3%
7 50
13.4%
8 70
18.7%
ValueCountFrequency (%)
8 70
18.7%
7 50
13.4%
6 46
12.3%
5 67
17.9%
4 70
18.7%
3 71
19.0%

bmi_category
Categorical

Distinct4
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
Normal
195 
Overweight
148 
Normal Weight
21 
Obese
 
10

Length

Max length13
Median length6
Mean length7.9491979
Min length5

Characters and Unicode

Total characters2973
Distinct characters18
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOverweight
2nd rowNormal
3rd rowNormal
4th rowObese
5th rowObese

Common Values

ValueCountFrequency (%)
Normal 195
52.1%
Overweight 148
39.6%
Normal Weight 21
 
5.6%
Obese 10
 
2.7%

Length

2023-09-18T14:56:09.205050image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T14:56:09.510345image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
normal 216
54.7%
overweight 148
37.5%
weight 21
 
5.3%
obese 10
 
2.5%

Most occurring characters

ValueCountFrequency (%)
r 364
12.2%
e 337
11.3%
N 216
 
7.3%
m 216
 
7.3%
a 216
 
7.3%
l 216
 
7.3%
o 216
 
7.3%
h 169
 
5.7%
t 169
 
5.7%
i 169
 
5.7%
Other values (8) 685
23.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2557
86.0%
Uppercase Letter 395
 
13.3%
Space Separator 21
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
r 364
14.2%
e 337
13.2%
m 216
8.4%
a 216
8.4%
l 216
8.4%
o 216
8.4%
h 169
6.6%
t 169
6.6%
i 169
6.6%
g 169
6.6%
Other values (4) 316
12.4%
Uppercase Letter
ValueCountFrequency (%)
N 216
54.7%
O 158
40.0%
W 21
 
5.3%
Space Separator
ValueCountFrequency (%)
21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2952
99.3%
Common 21
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
r 364
12.3%
e 337
11.4%
N 216
 
7.3%
m 216
 
7.3%
a 216
 
7.3%
l 216
 
7.3%
o 216
 
7.3%
h 169
 
5.7%
t 169
 
5.7%
i 169
 
5.7%
Other values (7) 664
22.5%
Common
ValueCountFrequency (%)
21
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2973
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
r 364
12.2%
e 337
11.3%
N 216
 
7.3%
m 216
 
7.3%
a 216
 
7.3%
l 216
 
7.3%
o 216
 
7.3%
h 169
 
5.7%
t 169
 
5.7%
i 169
 
5.7%
Other values (8) 685
23.0%

blood_pressure
Categorical

Distinct25
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
130/85
99 
140/95
65 
125/80
65 
120/80
45 
115/75
32 
Other values (20)
68 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters2244
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.8%

Sample

1st row126/83
2nd row125/80
3rd row125/80
4th row140/90
5th row140/90

Common Values

ValueCountFrequency (%)
130/85 99
26.5%
140/95 65
17.4%
125/80 65
17.4%
120/80 45
12.0%
115/75 32
 
8.6%
135/90 27
 
7.2%
140/90 4
 
1.1%
125/82 4
 
1.1%
132/87 3
 
0.8%
128/85 3
 
0.8%
Other values (15) 27
 
7.2%

Length

2023-09-18T14:56:09.906964image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
130/85 99
26.5%
125/80 65
17.4%
140/95 65
17.4%
120/80 45
12.0%
115/75 32
 
8.6%
135/90 27
 
7.2%
140/90 4
 
1.1%
125/82 4
 
1.1%
132/87 3
 
0.8%
128/85 3
 
0.8%
Other values (15) 27
 
7.2%

Most occurring characters

ValueCountFrequency (%)
1 420
18.7%
/ 374
16.7%
0 357
15.9%
5 333
14.8%
8 244
10.9%
3 139
 
6.2%
2 137
 
6.1%
9 107
 
4.8%
4 75
 
3.3%
7 49
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1870
83.3%
Other Punctuation 374
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 420
22.5%
0 357
19.1%
5 333
17.8%
8 244
13.0%
3 139
 
7.4%
2 137
 
7.3%
9 107
 
5.7%
4 75
 
4.0%
7 49
 
2.6%
6 9
 
0.5%
Other Punctuation
ValueCountFrequency (%)
/ 374
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2244
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 420
18.7%
/ 374
16.7%
0 357
15.9%
5 333
14.8%
8 244
10.9%
3 139
 
6.2%
2 137
 
6.1%
9 107
 
4.8%
4 75
 
3.3%
7 49
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2244
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 420
18.7%
/ 374
16.7%
0 357
15.9%
5 333
14.8%
8 244
10.9%
3 139
 
6.2%
2 137
 
6.1%
9 107
 
4.8%
4 75
 
3.3%
7 49
 
2.2%

heart_rate
Real number (ℝ)

Distinct19
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.165775
Minimum65
Maximum86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-09-18T14:56:10.404737image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum65
5-th percentile65
Q168
median70
Q372
95-th percentile78
Maximum86
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1356755
Coefficient of variation (CV)0.058941493
Kurtosis2.2864547
Mean70.165775
Median Absolute Deviation (MAD)2
Skewness1.2248235
Sum26242
Variance17.103812
MonotonicityNot monotonic
2023-09-18T14:56:10.856713image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
68 94
25.1%
70 76
20.3%
72 69
18.4%
65 67
17.9%
75 36
 
9.6%
78 5
 
1.3%
85 3
 
0.8%
80 3
 
0.8%
84 2
 
0.5%
83 2
 
0.5%
Other values (9) 17
 
4.5%
ValueCountFrequency (%)
65 67
17.9%
67 2
 
0.5%
68 94
25.1%
69 2
 
0.5%
70 76
20.3%
72 69
18.4%
73 2
 
0.5%
74 2
 
0.5%
75 36
 
9.6%
76 2
 
0.5%
ValueCountFrequency (%)
86 2
 
0.5%
85 3
0.8%
84 2
 
0.5%
83 2
 
0.5%
82 1
 
0.3%
81 2
 
0.5%
80 3
0.8%
78 5
1.3%
77 2
 
0.5%
76 2
 
0.5%

daily_steps
Real number (ℝ)

Distinct20
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6816.8449
Minimum3000
Maximum10000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.0 KiB
2023-09-18T14:56:11.326970image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum3000
5-th percentile4930
Q15600
median7000
Q38000
95-th percentile10000
Maximum10000
Range7000
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation1617.9157
Coefficient of variation (CV)0.23734084
Kurtosis-0.3940306
Mean6816.8449
Median Absolute Deviation (MAD)1000
Skewness0.17827733
Sum2549500
Variance2617651.1
MonotonicityNot monotonic
2023-09-18T14:56:11.798007image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
8000 101
27.0%
6000 68
18.2%
5000 68
18.2%
7000 66
17.6%
10000 36
 
9.6%
5500 4
 
1.1%
3000 3
 
0.8%
3500 3
 
0.8%
4000 3
 
0.8%
6800 3
 
0.8%
Other values (10) 19
 
5.1%
ValueCountFrequency (%)
3000 3
 
0.8%
3300 2
 
0.5%
3500 3
 
0.8%
3700 2
 
0.5%
4000 3
 
0.8%
4100 2
 
0.5%
4200 2
 
0.5%
4800 2
 
0.5%
5000 68
18.2%
5200 2
 
0.5%
ValueCountFrequency (%)
10000 36
 
9.6%
8000 101
27.0%
7500 2
 
0.5%
7300 2
 
0.5%
7000 66
17.6%
6800 3
 
0.8%
6200 1
 
0.3%
6000 68
18.2%
5600 2
 
0.5%
5500 4
 
1.1%

sleep_disorder
Categorical

Distinct3
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size3.0 KiB
None
219 
Sleep Apnea
78 
Insomnia
77 

Length

Max length11
Median length4
Mean length6.2834225
Min length4

Characters and Unicode

Total characters2350
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNone
2nd rowNone
3rd rowNone
4th rowSleep Apnea
5th rowSleep Apnea

Common Values

ValueCountFrequency (%)
None 219
58.6%
Sleep Apnea 78
 
20.9%
Insomnia 77
 
20.6%

Length

2023-09-18T14:56:12.387277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-18T14:56:13.016316image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
none 219
48.5%
sleep 78
 
17.3%
apnea 78
 
17.3%
insomnia 77
 
17.0%

Most occurring characters

ValueCountFrequency (%)
e 453
19.3%
n 451
19.2%
o 296
12.6%
N 219
9.3%
p 156
 
6.6%
a 155
 
6.6%
S 78
 
3.3%
l 78
 
3.3%
78
 
3.3%
A 78
 
3.3%
Other values (4) 308
13.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1820
77.4%
Uppercase Letter 452
 
19.2%
Space Separator 78
 
3.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 453
24.9%
n 451
24.8%
o 296
16.3%
p 156
 
8.6%
a 155
 
8.5%
l 78
 
4.3%
s 77
 
4.2%
m 77
 
4.2%
i 77
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
N 219
48.5%
S 78
 
17.3%
A 78
 
17.3%
I 77
 
17.0%
Space Separator
ValueCountFrequency (%)
78
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2272
96.7%
Common 78
 
3.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 453
19.9%
n 451
19.9%
o 296
13.0%
N 219
9.6%
p 156
 
6.9%
a 155
 
6.8%
S 78
 
3.4%
l 78
 
3.4%
A 78
 
3.4%
I 77
 
3.4%
Other values (3) 231
10.2%
Common
ValueCountFrequency (%)
78
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2350
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 453
19.3%
n 451
19.2%
o 296
12.6%
N 219
9.3%
p 156
 
6.6%
a 155
 
6.6%
S 78
 
3.3%
l 78
 
3.3%
78
 
3.3%
A 78
 
3.3%
Other values (4) 308
13.1%

Interactions

2023-09-18T14:56:00.332747image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:36.683369image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:40.658488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:43.479209image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:46.622416image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:48.963190image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:51.326620image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:54.398417image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:57.272163image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:56:00.625742image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:37.210425image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:40.913708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:43.881930image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:46.874011image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:49.229028image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:52.312944image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:54.677255image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:57.674572image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:56:00.931760image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:37.666067image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:41.205004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:44.297353image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:47.142422image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:49.531131image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:52.593623image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:54.933421image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:58.079694image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:56:01.206273image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:38.334953image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:41.459830image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:44.690762image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:47.424434image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:49.805362image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:52.857226image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:55.196738image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:58.450430image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:56:01.475248image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:38.851157image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:41.718872image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:45.109245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:47.674902image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:50.077745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:53.118716image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:55.448655image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:58.836488image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:56:01.731785image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:39.369504image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:41.983645image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:45.553481image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:47.952502image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:50.318678image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:53.376436image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:55.699532image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:59.264598image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:56:02.018692image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:39.833739image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:42.336453image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:45.834808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:48.217018image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:50.565773image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:53.643756image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:56.099687image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:59.596963image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:56:02.290411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:40.145852image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:42.693508image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:46.100294image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:48.469274image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:50.822450image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:53.881257image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:56.502803image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:59.822260image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:56:02.541625image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:40.394241image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:43.061115image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:46.352649image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:48.702708image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:51.065505image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:54.131610image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:55:56.889004image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2023-09-18T14:56:00.068440image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2023-09-18T14:56:13.274781image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Unnamed: 0person_idagesleep_durationquality_of_sleepphysical_activity_levelstress_levelheart_ratedaily_stepsgenderoccupationbmi_categoryblood_pressuresleep_disorder
Unnamed: 01.0001.0000.9990.3160.4210.143-0.425-0.251-0.0380.7070.5480.4420.5470.674
person_id1.0001.0000.9990.3160.4210.143-0.425-0.251-0.0380.7070.5480.4420.5470.674
age0.9990.9991.0000.3120.4180.136-0.426-0.254-0.0420.6940.5270.4850.5680.658
sleep_duration0.3160.3160.3121.0000.8870.209-0.811-0.6090.0100.5910.4780.4810.5340.673
quality_of_sleep0.4210.4210.4180.8871.0000.178-0.908-0.7350.0230.4640.6070.4290.7720.444
physical_activity_level0.1430.1430.1360.2090.1781.000-0.0170.1610.7940.0000.4520.5490.8100.626
stress_level-0.425-0.425-0.426-0.811-0.908-0.0171.0000.8200.1720.6930.6600.3070.6850.556
heart_rate-0.251-0.251-0.254-0.609-0.7350.1610.8201.0000.0940.7130.5040.6620.7760.558
daily_steps-0.038-0.038-0.0420.0100.0230.7940.1720.0941.0000.6980.5800.8020.8700.704
gender0.7070.7070.6940.5910.4640.0000.6930.7130.6981.0000.8420.3890.8000.374
occupation0.5480.5480.5270.4780.6070.4520.6600.5040.5800.8421.0000.5810.6840.734
bmi_category0.4420.4420.4850.4810.4290.5490.3070.6620.8020.3890.5811.0000.8550.569
blood_pressure0.5470.5470.5680.5340.7720.8100.6850.7760.8700.8000.6840.8551.0000.738
sleep_disorder0.6740.6740.6580.6730.4440.6260.5560.5580.7040.3740.7340.5690.7381.000

Missing values

2023-09-18T14:56:02.960909image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-18T14:56:03.488780image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Unnamed: 0person_idgenderageoccupationsleep_durationquality_of_sleepphysical_activity_levelstress_levelbmi_categoryblood_pressureheart_ratedaily_stepssleep_disorder
001Male27Software Engineer6.16426Overweight126/83774200None
112Male28Doctor6.26608Normal125/807510000None
223Male28Doctor6.26608Normal125/807510000None
334Male28Sales Representative5.94308Obese140/90853000Sleep Apnea
445Male28Sales Representative5.94308Obese140/90853000Sleep Apnea
556Male28Software Engineer5.94308Obese140/90853000Insomnia
667Male29Teacher6.36407Obese140/90823500Insomnia
778Male29Doctor7.87756Normal120/80708000None
889Male29Doctor7.87756Normal120/80708000None
9910Male29Doctor7.87756Normal120/80708000None
Unnamed: 0person_idgenderageoccupationsleep_durationquality_of_sleepphysical_activity_levelstress_levelbmi_categoryblood_pressureheart_ratedaily_stepssleep_disorder
364364365Female59Nurse8.09753Overweight140/95687000Sleep Apnea
365365366Female59Nurse8.09753Overweight140/95687000Sleep Apnea
366366367Female59Nurse8.19753Overweight140/95687000Sleep Apnea
367367368Female59Nurse8.09753Overweight140/95687000Sleep Apnea
368368369Female59Nurse8.19753Overweight140/95687000Sleep Apnea
369369370Female59Nurse8.19753Overweight140/95687000Sleep Apnea
370370371Female59Nurse8.09753Overweight140/95687000Sleep Apnea
371371372Female59Nurse8.19753Overweight140/95687000Sleep Apnea
372372373Female59Nurse8.19753Overweight140/95687000Sleep Apnea
373373374Female59Nurse8.19753Overweight140/95687000Sleep Apnea